Tumor resistance to radiation therapy remains a significant obstacle to long-term cancer patient survival, especially for head and neck squamous cell carcinoma (HNSCC), a cancer type with poor long-term outcomes (less than 50% advanced stage five-year survival). To overcome the problem of radiation resistance, radiation therapy is being combined with radiation-sensitizing chemotherapies. Prediction of an individual?s sensitivity to radiation and specific chemotherapy-radiation combination therapies prior to treatment would improve the development of personalized treatment plans for cancer patients. Efforts are being made to create systems biology models of cancer cells for biomarker discovery and prediction of treatment response; however, due to methodological shortcomings, failure to integrate multi-omic data on a genome-scale, and lack of specificity to individual patient tumors, these predictive models have yet to be implemented clinically. To address these needs, the objective of this project is to develop a personalized systems biology modeling platform for individualized prediction of HNSCC patient tumor response to radiation and chemotherapy-radiation combination therapies. These models will be created by first integrating comprehensive biological data on individual patients from The Cancer Genome Atlas (TCGA). This approach will allow for the comparison of metabolic, signaling, and phenotypic signatures between radiation-sensitive and radiation-resistant patient tumors. By then integrating the mechanisms of action of radiation therapy and radiation-sensitizing chemotherapies into the modeling framework, the response to particular chemotherapy-radiation combination therapies in individual radiation- resistant patient tumors can be predicted. Machine learning classifiers will be developed from TCGA patient data and model predictions to determine which biological and clinical factors are most predictive of radiation sensitivity and chemotherapy-radiation combination therapy success. It is hypothesized that differential response to chemotherapy-radiation combination therapies in radiation-resistant HNSCC tumors is accomplished through redox metabolism and signaling, and components of redox biology within the modeling framework will significantly enrich the list of predictive biomarkers for combination therapy success. Although the focus of this project will be on HNSCC, this systems biology modeling approach will be applicable to any cancer type. The outcomes of this project will be a reduced set of clinically-measurable biomarkers for accurate prediction of HNSCC patient response to radiation therapy and specific chemotherapy-radiation combination therapies, as well as a precision medicine platform to test clinically relevant therapeutic strategies. This project is innovative because it combines multi-omic cancer patient data with state-of-the-art systems biology modeling techniques to investigate the biological mechanisms of radiation resistance, as well as to predict chemotherapy-radiation combination therapy response in individual radiation-resistant patients.